O$ Memory

Research on memory-efficient computation focuses on mitigating the high memory demands of algorithms crucial for various machine learning tasks, particularly those involving long sequences. Current efforts concentrate on developing alternative architectures, such as those based on Holographic Reduced Representations, and refining existing algorithms like self-attention to reduce memory complexity from quadratic to logarithmic or even constant scaling with sequence length. These advancements are significant because they enable the application of powerful deep learning models to significantly larger datasets and longer sequences, impacting fields ranging from malware detection to natural language processing.